You need to sign in or sign up before continuing.
test_matmul_op.py 8.4 KB
Newer Older
1
#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
D
dzhwinter 已提交
2
#
D
dzhwinter 已提交
3 4 5
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
D
dzhwinter 已提交
6
#
D
dzhwinter 已提交
7
#     http://www.apache.org/licenses/LICENSE-2.0
D
dzhwinter 已提交
8
#
D
dzhwinter 已提交
9 10 11 12 13 14
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

15 16
from __future__ import print_function

M
Markus Kliegl 已提交
17 18
import unittest
import numpy as np
19
from op_test import OpTest
20 21
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
M
Markus Kliegl 已提交
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67


def generate_compatible_shapes(dim_X, dim_Y, transpose_X, transpose_Y):
    BATCH_SIZE = 2
    M = 3
    N = 4
    K = 5
    if (dim_X == 1 and transpose_X) or (dim_Y == 1 and transpose_Y):
        K = 1
    if dim_X == 1:
        if transpose_X:
            shape_X = [M]
        else:
            shape_X = [K]
    if dim_Y == 1:
        if transpose_Y:
            shape_Y = [N]
        else:
            shape_Y = [K]
    if dim_X >= 2:
        if transpose_X:
            shape_X = [K, M]
        else:
            shape_X = [M, K]
    if dim_X == 3:
        shape_X = [BATCH_SIZE] + shape_X
    if dim_Y >= 2:
        if transpose_Y:
            shape_Y = [N, K]
        else:
            shape_Y = [K, N]
    if dim_Y == 3:
        shape_Y = [BATCH_SIZE] + shape_Y
    return shape_X, shape_Y


def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
    """Reference forward implementation using np.matmul."""
    # np.matmul does not support the transpose flags, so we manually
    # transpose X and Y appropriately.
    if transpose_X:
        if X.ndim == 1:
            X = X.reshape((X.size, 1))
        elif X.ndim == 2:
            X = X.T
        else:
C
chengduoZH 已提交
68 69 70
            dim = [i for i in range(len(X.shape))]
            dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
            X = np.transpose(X, tuple(dim))
M
Markus Kliegl 已提交
71 72 73 74
    if transpose_Y:
        if Y.ndim == 1:
            Y = Y.reshape((1, Y.size))
        else:
C
chengduoZH 已提交
75 76 77 78
            dim = [i for i in range(len(Y.shape))]
            dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
            Y = np.transpose(Y, tuple(dim))

M
Markus Kliegl 已提交
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102
    Out = np.matmul(X, Y)
    if not Out.shape:
        # We do not support 0-dimensional Tensors (scalars). So where
        # np.matmul outputs a scalar, we must convert to a Tensor of
        # shape (1, ) instead.
        # Everywhere else, we are compatible with np.matmul.
        Out = np.array([Out], dtype="float32")
    return Out


class Generator(object):
    def setUp(self):
        self.op_type = "matmul"
        X = np.random.random(self.shape_X).astype("float32")
        Y = np.random.random(self.shape_Y).astype("float32")
        Out = reference_matmul(X, Y, self.transpose_X, self.transpose_Y)
        self.inputs = {'X': X, 'Y': Y}
        self.attrs = {
            'transpose_X': self.transpose_X,
            'transpose_Y': self.transpose_Y
        }
        self.outputs = {'Out': Out}

    def test_check_output(self):
103
        self.check_output(atol=1e-3)
M
Markus Kliegl 已提交
104 105

    def test_check_grad_normal(self):
106
        self.check_grad(['X', 'Y'], 'Out', max_relative_error=1e-3)
M
Markus Kliegl 已提交
107 108 109

    def test_check_grad_ignore_x(self):
        self.check_grad(
110
            ['Y'], 'Out', max_relative_error=1e-3, no_grad_set=set("X"))
M
Markus Kliegl 已提交
111 112 113

    def test_check_grad_ignore_y(self):
        self.check_grad(
114
            ['X'], 'Out', max_relative_error=1e-3, no_grad_set=set('Y'))
M
Markus Kliegl 已提交
115 116


117 118 119 120 121 122 123 124 125 126 127 128 129 130 131
class TestMatmulOpError(OpTest):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # The inputs type of matmul_op must be Variable.
            input1 = 12
            self.assertRaises(TypeError, fluid.layers.matmul, input1, input1)
            # The inputs dtype of matmul_op must be float32, float64.
            input2 = fluid.layers.data(
                name='input2', shape=[10, 10], dtype="int32")
            self.assertRaises(TypeError, fluid.layers.matmul, input2, input2)
            input3 = fluid.layers.data(
                name='input3', shape=[2, 2], dtype="float16")
            fluid.layers.matmul(input3, input3)


132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185
# Negative dimension generation
def generate_negative_dims(in_shape):
    from itertools import combinations
    size = len(in_shape)
    indexs = list()
    shapes = list()
    for i in range(size):
        indexs.extend(list(combinations([j for j in range(size)], i + 1)))
    for idx in indexs:
        shapes.append(
            [in_shape[i] if i not in idx else -1 for i in range(size)])
    return shapes


# Build program with inputs sizes that contain negative numbers
def test_negative_dims_program(obj):
    for shape_x in generate_negative_dims(obj.shape_X):
        for shape_y in generate_negative_dims(obj.shape_Y):
            X = np.random.random(obj.shape_X).astype("float32")
            Y = np.random.random(obj.shape_Y).astype("float32")
            Ref = reference_matmul(X, Y, obj.transpose_X, obj.transpose_Y)
            with program_guard(Program(), Program()):
                x = fluid.data(name='x', shape=shape_x, dtype='float32')
                y = fluid.data(name='y', shape=shape_y, dtype='float32')
                output = fluid.layers.matmul(x, y, obj.transpose_X,
                                             obj.transpose_Y)
                obj.assertEqual(len(Ref.shape), len(output.shape))
                for idx in range(len(Ref.shape)):
                    if output.shape[idx] != -1:
                        obj.assertEqual(Ref.shape[idx], output.shape[idx])
                exe = fluid.Executor(fluid.CPUPlace())
                res, = exe.run(fluid.default_main_program(),
                               feed={'x': X,
                                     'y': Y},
                               fetch_list=[output])
                np.allclose(res, Ref, atol=1e-5)


# Generate program api cases for all negative possibilities
def api_test(dim_x, dim_y, trans_x, trans_y):
    test_name = ('TestMatMulAPI_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
        dim_x, dim_y, trans_x, trans_y))
    shape_x, shape_y = generate_compatible_shapes(dim_x, dim_y, trans_x,
                                                  trans_y)
    globals()[test_name] = type(test_name, (OpTest, ), {
        'shape_X': shape_x,
        'shape_Y': shape_y,
        'transpose_X': trans_x,
        'transpose_Y': trans_y,
        'test_propram': test_negative_dims_program,
    })


# Generate operators cases for all possibilities
Y
Yu Yang 已提交
186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203
def inject_test(dim_x, dim_y, trans_x, trans_y):
    test_name = ('TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
        dim_x, dim_y, trans_x, trans_y))
    shape_x, shape_y = generate_compatible_shapes(dim_x, dim_y, trans_x,
                                                  trans_y)
    globals()[test_name] = type(test_name, (Generator, OpTest), {
        'shape_X': shape_x,
        'shape_Y': shape_y,
        'transpose_X': trans_x,
        'transpose_Y': trans_y,
    })


for dim_X in (1, 2, 3):
    for dim_Y in (1, 2, 3):
        for transose_x in (False, True):
            for transose_y in (False, True):
                inject_test(dim_X, dim_Y, transose_x, transose_y)
204
                api_test(dim_X, dim_Y, transose_x, transose_y)
C
chengduoZH 已提交
205 206


C
chengduoZH 已提交
207
# Test case n-dim
C
chengduoZH 已提交
208 209 210 211 212 213 214 215
def generate_compatible_shapes(dim, transpose_X, transpose_Y):
    M = 2
    N = 4
    K = 3
    shape_X = [2 for _ in range(dim - 2)]
    shape_Y = [2 for _ in range(dim - 2)]

    if transpose_X:
C
chengduoZH 已提交
216
        shape_X += [K, M]
C
chengduoZH 已提交
217
    else:
C
chengduoZH 已提交
218
        shape_X += [M, K]
C
chengduoZH 已提交
219 220

    if transpose_Y:
C
chengduoZH 已提交
221
        shape_Y += [N, K]
C
chengduoZH 已提交
222
    else:
C
chengduoZH 已提交
223
        shape_Y += [K, N]
C
chengduoZH 已提交
224 225 226 227

    return shape_X, shape_Y


Y
Yu Yang 已提交
228
# # Test case n-dim
C
chengduoZH 已提交
229 230 231 232 233 234 235 236 237 238 239 240 241 242
for dim in [4]:
    for transpose_X in [False, True]:
        for transpose_Y in [False, True]:
            test_name = (
                'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
                    dim, dim, transpose_X, transpose_Y))
            shape_X, shape_Y = generate_compatible_shapes(dim, transpose_X,
                                                          transpose_Y)
            globals()[test_name] = type(test_name, (Generator, OpTest), {
                'shape_X': shape_X,
                'shape_Y': shape_Y,
                'transpose_X': transpose_X,
                'transpose_Y': transpose_Y,
            })
C
chengduoZH 已提交
243

M
Markus Kliegl 已提交
244 245
if __name__ == "__main__":
    unittest.main()